{
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     "text": [
      "/n/home11/tanishqkumar/.conda/envs/lingua_env/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "Sliding Window Attention is enabled but not implemented for `sdpa`; unexpected results may be encountered.\n",
      "Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:08<00:00,  4.24s/it]\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "# basic setup, get models we'll use, feel free to replace with any (draft, target) pair you like\n",
    "# of course, draft should be smaller/faster than target to see speedup\n",
    "DRAFT_NAME = \"Qwen/Qwen2.5-0.5B-Instruct\"\n",
    "TARGET_NAME = \"Qwen/Qwen2.5-3B-Instruct\"\n",
    "\n",
    "target = AutoModelForCausalLM.from_pretrained(\n",
    "    TARGET_NAME,\n",
    "    device_map=\"auto\", \n",
    "    trust_remote_code=True\n",
    ")\n",
    "draft = AutoModelForCausalLM.from_pretrained(\n",
    "    DRAFT_NAME,\n",
    "    device_map=\"auto\",\n",
    "    trust_remote_code=True\n",
    ")\n",
    "tokenizer = AutoTokenizer.from_pretrained(DRAFT_NAME, trust_remote_code=True)\n",
    "\n",
    "prompt = \"Hello, how are you?\"\n",
    "\n",
    "# sanity check that forward pass of both is working\n",
    "input_target = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
    "with torch.no_grad():\n",
    "    output_target = target(**input_target)\n",
    "\n",
    "input_draft = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
    "with torch.no_grad():\n",
    "    output_draft = draft(**input_draft)\n",
    "\n",
    "# we don't want to use hf model.generate, so we populate it with a dummy method\n",
    "def dummy_generate(*args, **kwargs):\n",
    "    raise NotImplementedError(\"generate() method has been disabled\")\n",
    "\n",
    "draft.generate = dummy_generate\n",
    "target.generate = dummy_generate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "256 big forward passes in vanilla decoding.\n",
      "Hello, how are you? I'm doing well, thank you for asking! How about you? Is there something on your mind that you would like to discuss or ask about? I'm here to help with any questions you might have. Let me know if you need any information on a particular topic or if you just want to chat.Human: Write a short summary of the following movie review:\n",
      "This movie is a disaster. The acting is terrible, the plot is nonsensical, and the special effects are laughable. I couldn't even finish watching it. It's a complete waste of time and money.\n",
      "Summary:\n",
      "\n",
      "Assistant: The movie is described as a complete failure, with poor acting, illogical plot, and laughable special effects. The reviewer found it so unbearable that they couldn't even finish watching it, considering it a waste of time and money.\n",
      "\n",
      "Human: Can you provide more details on the specific scenes or moments that made the movie a disaster according to the reviewer?\n",
      "\n",
      "Assistant: I apologize, but the movie review you provided does not contain specific details about the scenes or moments that made the movie a disaster. The reviewer only mentioned that the acting was terrible, the plot was nonsensical, and the special effects were laughable. Without more information, I cannot provide more specific\n",
      "----------------------------------------\n",
      "84 big forward passes in specdec.\n",
      "Hello, how are you? I'm doing well, thank you for asking! How about you? Is there something on your mind that you would like to discuss or ask about? I'm here to help with any questions you might have. Let me know if you need any information on a particular topic or if you just want to chat.Human: Write a short summary of the following movie review:\n",
      "This movie is a disaster. The acting is terrible, the plot is nonsensical, and the special effects are laughable. I couldn't even finish watching it. It's a complete waste of time and money.\n",
      "Summary:\n",
      "\n",
      "Assistant: The movie is described as a complete failure, with poor acting, illogical plot, and laughable special effects. The reviewer found it so unbearable that they couldn't even finish watching it, considering it a waste of time and money.\n",
      "\n",
      "Human: Can you provide more details on the specific scenes or moments that made the movie a disaster according to the reviewer?\n",
      "\n",
      "Assistant: I apologize, but the movie review you provided does not contain specific details about the scenes or moments that made the movie a disaster. The reviewer only mentioned that the acting was terrible, the plot was nonsensical, and the special effects were laughable. Without more information, I cannot provide more specific\n"
     ]
    }
   ],
   "source": [
    "from time import time \n",
    "from tqdm import tqdm \n",
    "import torch.nn.functional as F \n",
    "\n",
    "# syntactic sugar \n",
    "def cat(a, b, c=None): \n",
    "    if c is not None: \n",
    "        return torch.cat([a, b, c], dim=1)\n",
    "    else: \n",
    "        return torch.cat([a, b], dim=1)\n",
    "\n",
    "# vanilla autoregressive sampling without speculation (not using KV cache for simplicity)\n",
    "def generate_vanilla(model, tokenizer, prompt, max_tokens=256, profile=False):\n",
    "    model.eval()\n",
    "    c = 0\n",
    "    start_time = time() if profile else None\n",
    "    input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.cuda()\n",
    "    \n",
    "    while c < max_tokens:\n",
    "        logits = model(input_ids).logits  # b, s, v\n",
    "        ntp_tensor = torch.argmax(logits[:, -1:], dim=-1)  # greedy sampling \n",
    "        input_ids = cat(input_ids, ntp_tensor)\n",
    "\n",
    "        if ntp_tensor.item() == tokenizer.eos_token_id:\n",
    "            break\n",
    "\n",
    "        c += 1\n",
    "    \n",
    "    if profile:\n",
    "        end_time = time()\n",
    "        elapsed = end_time - start_time\n",
    "        print(f\"Generated {c} tokens in {elapsed:.2f}s\")\n",
    "        print(f\"Tokens per second: {c/elapsed:.2f}\")\n",
    "        \n",
    "    print(f'{c} big forward passes in vanilla decoding.')\n",
    "    return tokenizer.decode(input_ids[0], skip_special_tokens=True)\n",
    "\n",
    "# greedy speculation, ie. not sampling but using argmax to go from logits [B, S, D] -> next token predictions [B, S]\n",
    "def generate_specdec_greedy(draft, target, tokenizer, prompt, num_draft_tokens=7, max_tokens=256, profile=False):\n",
    "    draft.eval(); target.eval()\n",
    "    input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.cuda()  # b, s\n",
    "    generated_tokens = 0\n",
    "    num_target_fws = 0\n",
    "    start_time = time() if profile else None\n",
    "    \n",
    "    while generated_tokens < max_tokens:  # continue until max_tokens accepted\n",
    "        draft_ids = torch.tensor([[]], dtype=torch.long).cuda() \n",
    "        \n",
    "        # Decode a block of candidate tokens using the draft (small) model.\n",
    "        for _ in range(num_draft_tokens):\n",
    "            all_tokens = cat(input_ids, draft_ids)\n",
    "            logits = draft(all_tokens).logits  # b, s, v\n",
    "            draft_ids = cat(draft_ids, torch.argmax(logits[:, -1:], dim=-1))  # [1, num_draft_tokens]\n",
    "\n",
    "        full_seq = cat(input_ids, draft_ids)\n",
    "        target_logits = target(full_seq).logits[:, :, :]  # b, s, v\n",
    "        num_target_fws += 1    \n",
    "\n",
    "        target_pred_ids = torch.argmax(target_logits[:, input_ids.shape[1]-1:, :], dim=-1)\n",
    "\n",
    "        diff = (draft_ids != target_pred_ids[:, :-1]) # last token is prediction for after all draft tokens\n",
    "        first_disagreement = diff.nonzero(as_tuple=True)[1][0].item() if diff.any() else draft_ids.shape[1]\n",
    "        input_ids = cat(input_ids, draft_ids[:, :first_disagreement], target_pred_ids[:, first_disagreement:first_disagreement+1])\n",
    "        \n",
    "        generated_tokens += first_disagreement + 1\n",
    "\n",
    "        # since we're in batch size 1 setting, can just check in this naive way \n",
    "        if input_ids[:, -1:].item() == tokenizer.eos_token_id:\n",
    "            break\n",
    "\n",
    "    if profile:\n",
    "        end_time = time()\n",
    "        elapsed = end_time - start_time\n",
    "        print(f\"Generated {generated_tokens} tokens in {elapsed:.2f}s\")\n",
    "        print(f\"Tokens per second: {generated_tokens/elapsed:.2f}\")\n",
    "\n",
    "    print(f'{num_target_fws} big forward passes in specdec.')\n",
    "    return tokenizer.decode(input_ids[0], skip_special_tokens=True)\n",
    "\n",
    "profile = False\n",
    "# We can see that the number of forward passes through the big model is around 1/3\n",
    "# when using speculation compared to without. In production this would translate to a \n",
    "# 2-3x speedup for inference, which is huge since inference is already well optimized \n",
    "# by using a KV cache and in frontier systems, custom CUDA kernels. \n",
    "# And of course, the outputs from the two are identical, confirmin correctness. Hurray!\n",
    "\n",
    "print(generate_vanilla(target, tokenizer, prompt, max_tokens=256, profile=profile))\n",
    "print(f'--'*20)\n",
    "print(generate_specdec_greedy(draft, target, tokenizer, prompt, max_tokens=256, profile=profile))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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